A very common scenario that we have run across from our customers when speaking with them about BI projects, is "Where do I start"? Many IT professional and business professionals have an understanding of business intelligence systems and the power that they can bring to your organization and the value that they can bring an organization. Yet these are complex systems which can include data warehouses, analytics, reporting, workflow, business process management, portals, on and on. In fact, the Microsoft BI stack includes all of these through SQL Server, MOSS, Visual Studio, Proclarity and PerformancePoint Server.
So where to begin ... What are my data sources? Do they support the problem scenarios that I am trying to provide to the business? What should my UDM look like? Star schema or snowflake? How should I surface my heirarchies, KPIs, analytics ... ? How long is this going to take me?
For those that have been reading my blog over the past several months, you may have captured a few bits of information that I have released early before our official launch regarding Microsoft Enterprise Cube (MEC). This is a way in which Microsoft can provide for you a beginning baseline out of the box to begin the process of gaining insights into your customers, providing immediate ROI on your BI investments and lowering operating income.
These are all the lofty goals and intent of any BI project. Here Microsoft will provide data models to use for your staging and star schemas, preditictive analytics algorithms, KPIs, reports, etc. all based on industry standards from your particular industry.
Here is an example: With an MEC solution for Churn Management in a telecommunications customer, you would receive the Microsoft BI stack with these elements included:
1. Physical ODS & star schema2. Cubes to support the scenario3. KPIs & reports to measure the business4. UI integrated with Virtual Earth and Silverlight5. ETL processes to bring your existing data into an existing data mart6. High speed source data adapters
I'm going to post a few notes this month, I promise, on some techniques that can be used to map data sources to an existing data mart and how to address such issues. Without clean data properly summarized in your data mart, your business intelligence solution will not bring you proper value to your business users.